






"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	Optional[Any]				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    params

           __magic_name__      				=    np.array(UpperCamelCase_ )
           __magic_name__      				=    np.array([len(UpperCamelCase_ ) for t in data] )

           self.check()
           self.remove_long_sequences()
           self.remove_empty_sequences()
           self.remove_unknown_sequences()
           self.check()
           self.print_statistics()







    def __getitem__(		self :	List[str]				,    UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           return (self.token_ids[index], self.lengths[index])







    def __len__(		self :	str ):





           '''simple docstring'''

           return len(self.lengths )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           assert len(self.token_ids ) == len(self.lengths )
           assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    self.params.max_model_input_size
           __magic_name__      				=    self.lengths > max_len
           logger.info(f"""Splitting {sum(UpperCamelCase_ )} too long sequences.""" )

           def divide_chunks(UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	Union[str, Any] ):
                  return [l[i : i + n] for i in range(0				,    len(UpperCamelCase_ )				,    UpperCamelCase_ )]

           __magic_name__      				=    []
           __magic_name__      				=    []
           if self.params.mlm:
                  __magic_name__				,  __magic_name__      				=    self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
           else:
                  __magic_name__				,  __magic_name__      				=    self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

           for seq_, len_ in zip(self.token_ids				,    self.lengths ):
                  assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
                  if len_ <= max_len:
                         new_tok_ids.append(seq_ )
                         new_lengths.append(len_ )
                  else:
                         __magic_name__      				=    []
                         for sub_s in divide_chunks(seq_				,    max_len - 2 ):
                                if sub_s[0] != cls_id:
                                       __magic_name__      				=    np.insert(UpperCamelCase_				,    0				,    UpperCamelCase_ )
                                if sub_s[-1] != sep_id:
                                       __magic_name__      				=    np.insert(UpperCamelCase_				,    len(UpperCamelCase_ )				,    UpperCamelCase_ )
                                assert len(UpperCamelCase_ ) <= max_len
                                assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                                sub_seqs.append(UpperCamelCase_ )

                         new_tok_ids.extend(UpperCamelCase_ )
                         new_lengths.extend([len(UpperCamelCase_ ) for l in sub_seqs] )

           __magic_name__      				=    np.array(UpperCamelCase_ )
           __magic_name__      				=    np.array(UpperCamelCase_ )







    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    len(self )
           __magic_name__      				=    self.lengths > 1_1
           __magic_name__      				=    self.token_ids[indices]
           __magic_name__      				=    self.lengths[indices]
           __magic_name__      				=    len(self )
           logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""" )







    def 				a__			(		self :	int ):





           '''simple docstring'''

           if "unk_token" not in self.params.special_tok_ids:
                  return
           else:
                  __magic_name__      				=    self.params.special_tok_ids['unk_token']
           __magic_name__      				=    len(self )
           __magic_name__      				=    np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
           __magic_name__      				=    (unk_occs / self.lengths) < 0.5
           __magic_name__      				=    self.token_ids[indices]
           __magic_name__      				=    self.lengths[indices]
           __magic_name__      				=    len(self )
           logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).""" )







    def 				a__			(		self :	str ):





           '''simple docstring'''

           if not self.params.is_master:
                  return
           logger.info(f"""{len(self )} sequences""" )
           # data_len = sum(self.lengths)
           # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
           # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

           # unk_idx = self.params.special_tok_ids['unk_token']
           # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
           # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    [t[0] for t in batch]
           __magic_name__      				=    [t[1] for t in batch]
           assert len(UpperCamelCase_ ) == len(UpperCamelCase_ )

           # Max for paddings
           __magic_name__      				=    max(UpperCamelCase_ )

           # Pad token ids
           if self.params.mlm:
                  __magic_name__      				=    self.params.special_tok_ids['pad_token']
           else:
                  __magic_name__      				=    self.params.special_tok_ids['unk_token']
           __magic_name__      				=    [list(t.astype(UpperCamelCase_ ) ) + [pad_idx] * (max_seq_len_ - len(UpperCamelCase_ )) for t in token_ids]
           assert len(tk_ ) == len(UpperCamelCase_ )
           assert all(len(UpperCamelCase_ ) == max_seq_len_ for t in tk_ )

           __magic_name__      				=    torch.tensor(tk_ )  # (bs, max_seq_len_)
           __magic_name__      				=    torch.tensor(UpperCamelCase_ )  # (bs)
           return tk_t, lg_t







"""simple docstring"""
import argparse
import copy


def 							A						(				__snake_case: Union[str, Any]  )    ->		Tuple:

       """simple docstring"""





       __magic_name__      				=    {}

       with open(__snake_case  ) as f:
              for line in f:
                     if line.split()[0] not in dict_of_neighbours:
                            __magic_name__      				=    []
                            _list.append([line.split()[1], line.split()[2]]  )
                            __magic_name__      				=    _list
                     else:
                            dict_of_neighbours[line.split()[0]].append(
                                [line.split()[1], line.split()[2]]  )
                     if line.split()[1] not in dict_of_neighbours:
                            __magic_name__      				=    []
                            _list.append([line.split()[0], line.split()[2]]  )
                            __magic_name__      				=    _list
                     else:
                            dict_of_neighbours[line.split()[1]].append(
                                [line.split()[0], line.split()[2]]  )

       return dict_of_neighbours


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: str  )    ->		List[str]:

       """simple docstring"""





       with open(__snake_case  ) as f:
              __magic_name__      				=    f.read(1  )
       __magic_name__      				=    start_node

       __magic_name__      				=    []

       __magic_name__      				=    start_node

       __magic_name__      				=    0
       while visiting not in first_solution:
              __magic_name__      				=    1_0_0_0_0
              for k in dict_of_neighbours[visiting]:
                     if int(k[1]  ) < int(__snake_case  ) and k[0] not in first_solution:
                            __magic_name__      				=    k[1]
                            __magic_name__      				=    k[0]

              first_solution.append(__snake_case  )
              __magic_name__      				=    distance_of_first_solution + int(__snake_case  )
              __magic_name__      				=    best_node

       first_solution.append(__snake_case  )

       __magic_name__      				=    0
       for k in dict_of_neighbours[first_solution[-2]]:
              if k[0] == start_node:
                     break
              position += 1

       __magic_name__      				=    (
           distance_of_first_solution
           + int(dict_of_neighbours[first_solution[-2]][position][1]  )
           - 1_0_0_0_0
       )
       return first_solution, distance_of_first_solution


def 							A						(				__snake_case: Union[str, Any] ,     __snake_case: Optional[Any]  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    []

       for n in solution[1:-1]:
              __magic_name__      				=    solution.index(__snake_case  )
              for kn in solution[1:-1]:
                     __magic_name__      				=    solution.index(__snake_case  )
                     if n == kn:
                            continue

                     __magic_name__      				=    copy.deepcopy(__snake_case  )
                     __magic_name__      				=    kn
                     __magic_name__      				=    n

                     __magic_name__      				=    0

                     for k in _tmp[:-1]:
                            __magic_name__      				=    _tmp[_tmp.index(__snake_case  ) + 1]
                            for i in dict_of_neighbours[k]:
                                   if i[0] == next_node:
                                          __magic_name__      				=    distance + int(i[1]  )
                     _tmp.append(__snake_case  )

                     if _tmp not in neighborhood_of_solution:
                            neighborhood_of_solution.append(_tmp  )

       __magic_name__      				=    len(neighborhood_of_solution[0]  ) - 1

       neighborhood_of_solution.sort(key=lambda __snake_case  : x[index_of_last_item_in_the_list]  )
       return neighborhood_of_solution


def 							A						(				__snake_case: List[str] ,     __snake_case: Union[str, Any] ,     __snake_case: List[str] ,     __snake_case: Optional[int] ,     __snake_case: List[str]  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    1
       __magic_name__      				=    first_solution
       __magic_name__      				=    []
       __magic_name__      				=    distance_of_first_solution
       __magic_name__      				=    solution

       while count <= iters:
              __magic_name__      				=    find_neighborhood(__snake_case ,     __snake_case  )
              __magic_name__      				=    0
              __magic_name__      				=    neighborhood[index_of_best_solution]
              __magic_name__      				=    len(__snake_case  ) - 1

              __magic_name__      				=    False
              while not found:
                     __magic_name__      				=    0
                     while i < len(__snake_case  ):
                            if best_solution[i] != solution[i]:
                                   __magic_name__      				=    best_solution[i]
                                   __magic_name__      				=    solution[i]
                                   break
                            __magic_name__      				=    i + 1

                     if [first_exchange_node, second_exchange_node] not in tabu_list and [
                         second_exchange_node,
                         first_exchange_node,
                     ] not in tabu_list:
                            tabu_list.append([first_exchange_node, second_exchange_node]  )
                            __magic_name__      				=    True
                            __magic_name__      				=    best_solution[:-1]
                            __magic_name__      				=    neighborhood[index_of_best_solution][best_cost_index]
                            if cost < best_cost:
                                   __magic_name__      				=    cost
                                   __magic_name__      				=    solution
                     else:
                            __magic_name__      				=    index_of_best_solution + 1
                            __magic_name__      				=    neighborhood[index_of_best_solution]

              if len(__snake_case  ) >= size:
                     tabu_list.pop(0  )

              __magic_name__      				=    count + 1

       return best_solution_ever, best_cost


def 							A						(				__snake_case: Union[str, Any]=None  )    ->		Tuple:

       """simple docstring"""





       __magic_name__      				=    generate_neighbours(args.File  )

       __magic_name__				,  __magic_name__      				=    generate_first_solution(
           args.File ,     __snake_case  )

       __magic_name__				,  __magic_name__      				=    tabu_search(
           __snake_case ,     __snake_case ,     __snake_case ,     args.Iterations ,     args.Size ,     )

       print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""  )


if __name__ == "__main__":
 snake_case : str     		=		argparse.ArgumentParser(description="""Tabu Search""")
 parser.add_argument(
     """-f""",
     """--File""",
     type=str,
     help="""Path to the file containing the data""",
     required=True,
 )
 parser.add_argument(
     """-i""",
     """--Iterations""",
     type=int,
     help="""How many iterations the algorithm should perform""",
     required=True,
 )
 parser.add_argument(
     """-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
 )

 # Pass the arguments to main method
 main(parser.parse_args())